""" Res2Net and Res2NeXt
Adapted from Official Pytorch impl at: https://github.com/gasvn/Res2Net/
Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
"""
import math

import torch
import torch.nn as nn
import torch.nn.functional as F

from .resnet import ResNet, SEModule
from .registry import register_model
from .helpers import load_pretrained
from .adaptive_avgmax_pool import SelectAdaptivePool2d


IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)

__all__ = []


def _cfg(url='', **kwargs):
    return {
        'url': url,
        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
        'crop_pct': 0.875, 'interpolation': 'bilinear',
        'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
        'first_conv': 'conv1', 'classifier': 'fc',
        **kwargs
    }


default_cfgs = {
    'res2net50_26w_4s': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_4s-06e79181.pth'),
    'res2net50_48w_2s': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_48w_2s-afed724a.pth'),
    'res2net50_14w_8s': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_14w_8s-6527dddc.pth'),
    'res2net50_26w_6s': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_6s-19041792.pth'),
    'res2net50_26w_8s': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_8s-2c7c9f12.pth'),
    'res2net101_26w_4s': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net101_26w_4s-02a759a1.pth'),
    'res2next50': _cfg(
        url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next50_4s-6ef7e7bf.pth'),
}


class Bottle2neck(nn.Module):
    """ Res2Net/Res2NeXT Bottleneck
    Adapted from https://github.com/gasvn/Res2Net/blob/master/res2net.py
    """
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None,
                 cardinality=1, base_width=26, scale=4, use_se=False,
                 norm_layer=None, dilation=1, previous_dilation=1, **_):
        super(Bottle2neck, self).__init__()
        assert dilation == 1 and previous_dilation == 1  # FIXME support dilation
        self.scale = scale
        self.is_first = stride > 1 or downsample is not None
        self.num_scales = max(1, scale - 1)
        width = int(math.floor(planes * (base_width / 64.0))) * cardinality
        outplanes = planes * self.expansion
        self.width = width

        self.conv1 = nn.Conv2d(inplanes, width * scale, kernel_size=1, bias=False)
        self.bn1 = norm_layer(width * scale)

        convs = []
        bns = []
        for i in range(self.num_scales):
            convs.append(nn.Conv2d(
                width, width, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False))
            bns.append(norm_layer(width))
        self.convs = nn.ModuleList(convs)
        self.bns = nn.ModuleList(bns)
        if self.is_first:
            self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)

        self.conv3 = nn.Conv2d(width * scale, outplanes, kernel_size=1, bias=False)
        self.bn3 = norm_layer(outplanes)
        self.se = SEModule(outplanes, planes // 4) if use_se else None

        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        spx = torch.split(out, self.width, 1)
        spo = []
        for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
            sp = spx[i] if i == 0 or self.is_first else sp + spx[i]
            sp = conv(sp)
            sp = bn(sp)
            sp = self.relu(sp)
            spo.append(sp)
        if self.scale > 1 :
            spo.append(self.pool(spx[-1]) if self.is_first else spx[-1])
        out = torch.cat(spo, 1)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.se is not None:
            out = self.se(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


@register_model
def res2net50_26w_4s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a Res2Net-50_26w_4s model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    default_cfg = default_cfgs['res2net50_26w_4s']
    res2net_block_args = dict(scale=4)
    model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=26,
                   num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def res2net101_26w_4s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a Res2Net-50_26w_4s model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    default_cfg = default_cfgs['res2net101_26w_4s']
    res2net_block_args = dict(scale=4)
    model = ResNet(Bottle2neck, [3, 4, 23, 3], base_width=26,
                   num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def res2net50_26w_6s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a Res2Net-50_26w_4s model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    default_cfg = default_cfgs['res2net50_26w_6s']
    res2net_block_args = dict(scale=6)
    model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=26,
                   num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def res2net50_26w_8s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a Res2Net-50_26w_4s model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    default_cfg = default_cfgs['res2net50_26w_8s']
    res2net_block_args = dict(scale=8)
    model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=26,
                   num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def res2net50_48w_2s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a Res2Net-50_48w_2s model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    default_cfg = default_cfgs['res2net50_48w_2s']
    res2net_block_args = dict(scale=2)
    model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=48,
                   num_classes=num_classes, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def res2net50_14w_8s(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Constructs a Res2Net-50_14w_8s model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    default_cfg = default_cfgs['res2net50_14w_8s']
    res2net_block_args = dict(scale=8)
    model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=14, num_classes=num_classes, in_chans=in_chans,
                   block_args=res2net_block_args, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model


@register_model
def res2next50(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
    """Construct Res2NeXt-50 4s
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    default_cfg = default_cfgs['res2next50']
    res2net_block_args = dict(scale=4)
    model = ResNet(Bottle2neck, [3, 4, 6, 3], base_width=4, cardinality=8,
                   num_classes=1000, in_chans=in_chans, block_args=res2net_block_args, **kwargs)
    model.default_cfg = default_cfg
    if pretrained:
        load_pretrained(model, default_cfg, num_classes, in_chans)
    return model
